Risk prediction models for diabetic nephropathy among type 2 diabetes patients in China: a systematic review and meta-analysis - Xu, Wenbin; Zhou, Yanfei; Jiang, Qian; Fang, Yiqian; Yang, Qian (Frontiers in Endocrinology, 2024)
Objective: This study systematically reviews and meta-analyzes existing risk prediction model s for diabetic kidney disease (DKD) among patients with type 2 diabetes, aiming to provide references for scholars in China to develop higher-quality risk prediction models.: We searched databases including CNKI (China National Knowledge Infrastructure), Wanfang Data, VIP Chinese Science and Technology Journal Database, CBM (China Biome dical Literature Database), PubMed, Web of Science, Embase, and the Cochrane Library for studies on the construction of DKD risk prediction models among type 2 diabetes patients, u p until December 28, 2023. Two researchers independently screened the literature and extract ed and evaluated information according to a data extraction form and bias risk assessment to ol for prediction model studies. The area under the curve (AUC) values of the models were meta-analyzed using STATA 14.0 software.Results: A total of 32 studies were included, with 31 performing internal validation and 22 reporting calibration. The incidence rate of DKD among patients with type 2 diabetes ranged from 6.0% to 62.3%. The AUC ranged from 0.713 to 0.949, indicating the prediction mode ls have fair to excellent prediction accuracy. The overall applicability of the included studies was good; however, there was a high overall risk of bias, mainly due to the retrospective n ature of most studies, unreasonable sample sizes, and single-center studies. Meta-analysis of t he models yielded a combined AUC of 0.810 (95% CI: 0.780-0.840), indicating good predict ive performance.Research on DKD risk prediction models for patients with type 2 diabetes in C hina is still in its initial stages, with a high overall risk of bias and a lack of clinical applic ation. Future efforts could focus on constructing high-performance, easy-to-use prediction mod els based on interpretable machine learning methods and applying them in clinical settings.Registration：This systematic review and meta-analysis were conducted following the Preferr ed Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement, a recog nized guideline for such research. This meta-analysis was registered on PROSPERO. Number: CRD42024498015.